Running Efficiency Calculator
The formula
What the running efficiency tells you
A field index of speed per unit of cardiovascular strain — the watchless version of lab economy testing.
Calculation: Efficiency = speed per %HRmax demand — an EF-style drift index. Enter your inputs and every substitution is shown below the result.
Reading the result
Track the trend across weeks at the same heart rate; the absolute value is meaningless without your baseline.
Rules of thumb: 5:00/km at 82% HRmax ≈ index 14.6 for a recreational runner
Limitations and best practice
Heat, terrain and caffeine masquerade as lost fitness — compare like sessions on like days.
This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Frequently asked questions
Is this the same as running economy?
Why does my index drop in summer?
Source & methodology
Maffetone EF method; Seiler intensity distribution work.
Running Efficiency: what this tool answers
You are here because you need a specific number, and you want to see how it was produced before you rely on it. Searchers land on a Running Efficiency page for three reasons — to plan, to compare options, and to check that somebody else’s arithmetic is honest. This tool serves all three, and everything after the calculator explains the model instead of hiding it.
The number itself is the easy part. What takes judgement is knowing which inputs are facts in your situation and which are guesses wearing a decimal point — a distinction this page makes explicit for every field above.
It helps to separate the mechanics from the interpretation. The mechanics are fixed: Efficiency = speed per %HRmax demand — an EF-style drift index. Anyone, anywhere, entering the same inputs will get the same 14.1. The interpretation is where experience counts: whether 14.1 is good, normal or alarming depends on comparisons covered below, and that is the part most articles about Running Efficiency never explain.
What makes a calculation page trustworthy is that its copy is derived from its own arithmetic: here efficiency index = 14.1, recomputed every time a field changes, and the sensitivity section ranks the inputs that move the result — Average HR first, at ≈11.1 % of the headline for a ten-percent error either way in that input. Work down the page and by the end you could re-derive the result in a spreadsheet from memory.
Inside the Running Efficiency calculation: formula and every input
Here is the complete model this tool runs — Efficiency = speed per %HRmax demand — an EF-style drift index — with each input explained below. If a competitor’s page gives you a different answer from the same inputs, this is the text that settles which of you is right.
Distance (km) (example value 19) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. Measure twice and use the unit the label asks for — length terms often scale the result geometrically.
Average HR (bpm) (example value 155) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. Replace the example with the figure from your own records — the tool recomputes instantly and the printed steps follow your numbers.
HRmax (bpm) (example value 182) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. If you are unsure what belongs in this field, look at the printed steps: the substitution shows exactly how the engine uses it.
Pace (min/km) (example value 5) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. This input feeds the formula directly; a defensible figure here is worth more than false precision anywhere else.
The default Running Efficiency calculation, written out in full
Rather than describe the calculation, here it is end to end — every substitution in order, starting from the inputs above. Starting figures: Distance (km), Average HR (bpm), HRmax (bpm), Pace (min/km). The endpoint of that chain is 14.1 of efficiency index, sitting in the “You moved at 12.0 km/h using 85.2% of max HR — index 14.1; rising index at the same pace is fitness, falling index in the same session is drift” band.
- Speed = 60 ÷ 5.00 = 12.0 km/h
- %HRmax = 155 ÷ 182 = 85.2%
- Index = speed ÷ %HRmax × 100 = 14.1
Speed (km/h) — 12.0. Compare it against the main result of 14.1 before drawing conclusions.
% of HRmax used — 85.2. Read next to the headline, this is the figure that shows the shape of the trade-off.
Pace @85% HR — 4:59. This is the number experienced users quote when the headline alone would mislead.
Decoupling watch — room to hold pace. Compare it against the main result of 14.1 before drawing conclusions.
Re-running with your own figures is instant; re-running by hand once is the best way to know the tool is telling you the truth.
Scenario table: Running Efficiency at ±10 % and ±20 % of the key input
Read this table as the practical error bar on the headline figure. It answers the question people actually have after seeing a single number: how wrong could this be if the main input is off by ten or twenty percent?
| Average HR setting | Efficiency index |
|---|---|
| 80 % of the example value | 17.6 |
| 90 % of the example value | 15.7 |
| example inputs | 14.1 |
| 110 % of the example value | 12.8 |
| 120 % of the example value | 11.7 |
That spread — 11.7 at the low end, 17.6 at the high — is what a careful read of Running Efficiency looks like: a band you can state out loud, with the reason for each edge. Reproduce it in the form above by editing Average HR alone; the substitution steps will follow your figures exactly.
Where Running Efficiency is fragile — and where it is not
This list was computed for this specific engine just now: each numeric input was nudged ±10 % while every other field held its example value, and the efficiency index was recalculated from scratch. For Running Efficiency the ranking is measured rather than guessed, and it belongs to these defaults: shift the starting point and the order can shift with it.
- Nudge Average HR by ±10 % and Running Efficiency lands between 12.8 and 15.7 (sensitivity ≈11.1 %)
- Nudge Pace by ±10 % and Running Efficiency lands between 12.8 and 15.7 (sensitivity ≈11.1 %)
- HRmax +10 % → Running Efficiency = 15.5 · −10 % → 12.7 (the efficiency index shifts ≈10.0 %)
Average HR is the dominant lever here: ten percent either way moves the result by about 11.1 %, from 12.8 to 15.7. Measure that input from a document rather than from memory, and Pace is effectively tied with it (≈11.1 % for the same nudge), so verify the pair together. The rest of the inputs earn only a rounding-error of attention, and that asymmetry is the practical payoff of measuring rather than asserting.
What a Running Efficiency of 14.1 means in practice
A number on its own is trivia; a number against a range is intelligence. The default case lands in the “You moved at 12.0 km/h using 85.2% of max HR — index 14.1; rising index at the same pace is fitness, falling index in the same session is drift” band, and bands are deliberately wide because real situations overlap. The sensitivity list shows how far a modest input error moves you along that ladder: for Average HR, ±10 % covers 12.8 to 15.7. The practical reading rules: ask what would have to change to move you one band. If the answer is “a small, plausible input difference”, the band itself is not the finding — the uncertainty is.
Where Running Efficiency sits next to similar measures: this tool answers “how much is the Running Efficiency”, while the related calculators answer “what do I do about it”. Reading the result alone tends to over-weight a single snapshot; pairing it with the companion tools above turns the number into a decision.
How people get Running Efficiency wrong
Heat, terrain and caffeine masquerade as lost fitness — compare like sessions on like days. This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Reusing a Running Efficiency from last season. In Health & Medical the inputs are the thing that drifts; the formula does not.
Attention is also misallocated between fields. Having measured this engine, we can say where it belongs: Average HR first, everything else second.
Another is comparing across contexts: a Running Efficiency computed with one set of conventions should never be lined up against a figure computed with another. Check the inputs behind both before ranking them.
Before you trust your Running Efficiency: a short checklist
- Re-run the calculation with a ±10 % stress on the two most sensitive inputs.
- Record the date and the inputs alongside the number so future-you knows what was assumed.
- Check Average HR before anything else: it is the measured dominant input, ≈11.1 % of Running Efficiency per ten percent.
- Check that every field the formula needs actually arrived from a document, not from recollection.
- Recompute once with the dominant input deliberately set low, and see whether the decision changes.
- Run the ±10 % stress on the top two levers and check that your conclusion survives both.
A routine for decision-grade Running Efficiency
The tool does the arithmetic; the routine below is what makes the arithmetic trustworthy. Five steps, a minute total — and afterwards you will know not just what the number is, but which assumption produced it.
Step 1 — start from evidence. The inputs behind Running Efficiency are distance (km), average hr (bpm), hrmax (bpm), pace (min/km). Write them down with their source next to each one; if you cannot name where a figure came from, that is the input most likely to be wrong. Repeating a calculation with the same numbers proves only that the arithmetic is reliable — which is worth knowing.
Step 2 — compute the version you would not be embarrassed by. If a stranger audited your inputs, which set would you hand over? Enter that set, read the result, and treat everything else as upside. A figure with a date beside it becomes evidence; without one it stays an anecdote.
Step 3 — change one input at a time. Nudge a single field by ten percent, watch the headline and its companions move, then undo it. Two or three fields will dominate — the sensitivity table above already ranks them for this engine. The input you reach for first is usually the one you measured longest ago.
Step 4 — compute a corridor, not a point. Three runs — worst, likely, best — turn Running Efficiency from a snapshot into a range. Plan against the pessimistic end and treat the optimistic end as the upside story. Repeating a calculation with the same numbers proves only that the arithmetic is reliable — which is worth knowing.
Step 5 — save the story with the figure. A number without its inputs and its date is unusable within a quarter. Record 14.1, the assumptions behind it, and today’s date; your future self will be auditing a stranger’s arithmetic. Rounding early is the quietest way to introduce a permanent error.
Where Running Efficiency matters in practice
Numbers earn their keep in conversations, and Running Efficiency is no exception. In Health & Medical discussions around Fitness Performance, this figure decides whether an option stays on the table: it converts an opinionated debate into an arithmetic one, and arithmetic is the only referee both sides accept. Write the figure down before moving on; memory flatters whichever number you saw last.
The companion outputs — Speed (km/h) and % of HRmax used — exist for the second stage of that screen: the headline sizes the prize, while the companions describe the trade-off you are accepting to get it. A figure nobody can reproduce is a figure nobody will act on twice.
Nothing here is stored or transmitted, so the habit of checking is free: open the page, re-enter current figures, compare with the number you saved last time. Estimates improve most when the worst case is written down in ink.
People also ask about Running Efficiency
What affects Running Efficiency the most?
Running Efficiency is driven above all by Average HR. Perturbing only that field by ten percent either way produced 12.8 against the 14.1 baseline — a ≈11.1 % response, while Pace manages ≈11.1 %.
Do pace and average hr matter equally?
On these inputs, yes: the two measured within a rounding error of each other — ≈11.1 % and ≈11.1 % of the efficiency index per ten-percent change. Treat them as one pair to verify together rather than ranking them.
What is a typical Running Efficiency?
With the example inputs on this page the result is 14.1, labelled “You moved at 12.0 km/h using 85.2% of max HR — index 14.1; rising index at the same pace is fitness, falling index in the same session is drift”. “Typical” varies with situation, which is why every result here ships with its verdict band and its sensitivity list: they turn one figure into a range you can locate yourself inside.
How do I calculate Running Efficiency by hand?
Three steps: write the formula (Efficiency = speed per %HRmax demand — an EF-style drift index), insert your values, and simplify. The worked example section shows exactly that chain for the default inputs; replicating it once on paper teaches you more about Running Efficiency than any number of calculator runs.
What happens to Running Efficiency if average hr changes by 10 %?
The engine was re-run with that field moved by ten percent while everything else stayed fixed: the efficiency index goes across 12.8 to 15.7, a swing of roughly 11.1 % around the 14.1 baseline. That ratio is the practical definition of how much this input matters.
Is the Running Efficiency result of 14.1 good or bad?
Neither, on its own — the label “You moved at 12.0 km/h using 85.2% of max HR — index 14.1; rising index at the same pace is fitness, falling index in the same session is drift” is the band this figure falls in for the example inputs, and your own inputs will place you elsewhere on the same ladder. Judge the number against your history and your alternatives, and remember that a ten-percent error in Average HR alone moves it by ≈11.1 %.
How accurate is Running Efficiency for me personally?
The formula encodes averages; body-composition and measurement error typically shifts results by a few percent in either direction. Use ranges and trends, and treat any single 14.1 as approximate.
What affects Running Efficiency the most?
Sensitivity depends on where you start, but the inputs Distance (km) and Average HR (bpm) typically move Running Efficiency further than the rest because they enter the formula multiplicatively. If you can only measure two things well, measure those and hold the others at conservative defaults.
Is this the same as running economy?
It is the field cousin — economy needs a lab VO2 measure, this uses HR as the oxygen proxy.
Why does my index drop in summer?
HRat the same pace rises, so the denominator grows — the index is honest about the strain.
Is my data private on this page?
Yes — computation happens locally in your browser, with no account, no tracking of your inputs and nothing sent to a server. The printed formula is the whole story of what happens to your figures.
The vocabulary of Running Efficiency
- Speed (km/h)
- 12.0 in the default Running Efficiency calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- % of HRmax used
- 85.2 in the default Running Efficiency calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Pace @85% HR
- 4:59 on the example inputs — a companion figure printed beside the headline of Running Efficiency so that no result is read in isolation.
- Efficiency index
- The primary output of this tool: 14.1 for the default inputs, computed exactly from Efficiency = speed per %HRmax demand — an EF-style drift index.
- Worked example
- The calculation written out in full so that nothing on this page has to be taken on trust.
- Verdict band
- A named zone on the range of plausible results; the example inputs land in “You moved at 12.0 km/h using 85.2% of max HR — index 14.1; rising index at the same pace is fitness, falling index in the same session is drift”, and your inputs may not.
- Sensitivity (±10 %)
- How far the Running Efficiency moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
Where the Running Efficiency model comes from
Everything above runs on one stated formula, applied without hidden coefficients: Efficiency = speed per %HRmax demand — an EF-style drift index. Source of record: Maffetone EF method; Seiler intensity distribution work. Any other implementation of this model should return the same figure for the same inputs. If one does not, the difference is in its assumptions — which this page prints and that one probably does not.
Treat the output as a precise answer to the question you typed: exact arithmetic, transparent formula, published source, no hidden rounding. The sensitivity table above is part of that promise rather than a bonus: it lets you see how much of the result depends on how carefully you measured.
Next steps after the calculation
Re-run the calculation with your own figures, stress the two most sensitive inputs by ±10 %, and only then act on the result.
Whoever you are (people who need one trustworthy number before their next decision), the next step is the same: replace the example inputs with your own, note the date, and re-run whenever a figure moves. In Health & Medical, a dated figure is a trend, and an undated one is a rumour.